Self-organising Coordination

How to coordinate at best a set of autonomous software entities that need to achieve a collective goal, without centralised supervision? This is the question that our research in adaptive, self-organising, decentralised coordination mechanisms seeks to answer.

Motivation

Agents in multi-agent systems are usually equipped with pre-defined interaction means (e.g. messaging abilities) and fixed coordination protocols to abide to. This cannot cope with highly dynamic scenarios demanding for adaptation.

A way out is to let agents learn how to interact and coordinate at best.


Coordination in pervasive systems cannot be done by individually and imperatively programming each partecipating device: the levels of abstraction and autonomy are too low.

Ways to let devices autonomously figure out how to participate in a systemic goal given by designer, or arising dynamically according to context, must be found.

Approach

We deal with these open challenges by using techniques from

  • multi-agent reinforcement learning
  • causal reasoning
  • computational argumentation

Reference publications

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